Research map / Learning theory and algorithms / Fairness
Bounding the fairness and accuracy of classifiers from population statistics
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Lower bound computed by Alg. 1 instead of true discrepancy (tightness test) | linear regression classifiers on US Census · US Census (410 classifiers) | ratio of unfairness lower bound to true unfairness | Mixed |
Closest papers by meaning
- On Fairness and Calibration (NeurIPS 2017 · similarity 0.90)
- Why Is My Classifier Discriminatory? (NeurIPS 2018 · similarity 0.89)
- Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification (AAAI 2024 · similarity 0.88)
- How Far Can Fairness Constraints Help Recover From Biased Data? (ICML 2024 · similarity 0.86)
- Accuracy vs. Accuracy: Computational Tradeoffs Between Classification Rates and Utility (NeurIPS 2026 · similarity 0.86)
- Disparate Conditional Prediction in Multiclass Classifiers (ICML 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.